code: def test_wait_multiple_tensors():
      a = torch.rand(1, requires_grad=True)
      b = torch.rand(1, requires_grad=True)
  
      a, b = Wait.apply(CPUStream, CPUStream, a, b)
  
      assert a.grad_fn is b.grad_fn
      assert a.grad_fn.__class__ is Wait._backward_cls
  
file_path: fairscale/tests/nn/pipe/test_copy.py
imports: import pytest
  import torch
  from fairscale.nn.pipe.copy import Copy, Wait
  from fairscale.nn.pipe.stream import CPUStream, current_stream, get_device, is_cuda, new_stream, use_stream
label_desc: Use image node_id 6 for calling a global function with example usage: test_wait_multiple_tensors() without return types